We have focused on developing datasets for gene expression to study the effects of mutations in the genes associated with neurodegeneration including Alzheimers disease. Using RNA-Seq allows us to apply a standard set of methods to a variety of model systems. Using this approach we have contributed to a number of different studies. One of the most interesting areas is in the application to the human brain, where we have a large series of brains with information on both genetic variability and gene expression. Our data has been used in many studies to determine whether a nominated genetic variant associated with a given disease (such as Alzheimers) or other phenotype, has a proximal biological effect on gene expression. We have used our datasets to recently study variation around the SNCA gene, which encodes for the protein a-synuclein. GWAS nominated risk variants were associated with higher levels of specific mRNA transcripts arising from this gene. We are currently extending these efforts to include single cell level analyses.